{"title":"A generalized model selection framework for multi-state failure data analysis","authors":"R. Patil, S. Patil, G. Gupta, A. Bewoor","doi":"10.1108/ijqrm-08-2021-0280","DOIUrl":null,"url":null,"abstract":"PurposeThe purpose of this paper is to carry out a reliability analysis of a mechanical system considering the degraded states to get a proper understanding of system behavior and its propagation towards complete failure.Design/methodology/approachThe reliability analysis of computerized numerical control machine tools (CNCMTs) using a multi-state system (MSS) approach that considers various degraded states rather than a binary approach is carried out. The failures of the CNCMT are classified into five states: one fully operational state, three degraded states and one failed state.FindingsThe analysis of failure data collected from the field and tests conducted in the laboratory provided detailed understandings about the quality of the material and its failure behavior used in designing and the capability of the manufacturing system. The present work identified that Class II (major failure) is critical from a maintainability perspective whereas Class III (moderate failure) and Class IV (minor failure) are critical from a reliability perspective.Research limitations/implicationsThis research applies to reliability data analysis of systems that consider various degraded states.Practical implicationsMSS reliability analysis approach will help to identify various degraded states of the system that affect the performance and productivity and also to improve system reliability, availability and performance.Social implicationsIndustrial system designers recognized that reliability and maintainability is a critical design attribute. Reliability studies using the binary state approach are insufficient and incorrect for the systems with degraded failures states, and such analysis can give incorrect results, and increase the cost. The proposed MSS approach is more suitable for complex systems such as CNCMT rather than the binary-state system approach.Originality/valueThis paper presents a generalized framework MSS's failure and repair data analysis has been developed and applied to a CNCMT.","PeriodicalId":14193,"journal":{"name":"International Journal of Quality & Reliability Management","volume":" ","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2022-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Quality & Reliability Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/ijqrm-08-2021-0280","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 2
Abstract
PurposeThe purpose of this paper is to carry out a reliability analysis of a mechanical system considering the degraded states to get a proper understanding of system behavior and its propagation towards complete failure.Design/methodology/approachThe reliability analysis of computerized numerical control machine tools (CNCMTs) using a multi-state system (MSS) approach that considers various degraded states rather than a binary approach is carried out. The failures of the CNCMT are classified into five states: one fully operational state, three degraded states and one failed state.FindingsThe analysis of failure data collected from the field and tests conducted in the laboratory provided detailed understandings about the quality of the material and its failure behavior used in designing and the capability of the manufacturing system. The present work identified that Class II (major failure) is critical from a maintainability perspective whereas Class III (moderate failure) and Class IV (minor failure) are critical from a reliability perspective.Research limitations/implicationsThis research applies to reliability data analysis of systems that consider various degraded states.Practical implicationsMSS reliability analysis approach will help to identify various degraded states of the system that affect the performance and productivity and also to improve system reliability, availability and performance.Social implicationsIndustrial system designers recognized that reliability and maintainability is a critical design attribute. Reliability studies using the binary state approach are insufficient and incorrect for the systems with degraded failures states, and such analysis can give incorrect results, and increase the cost. The proposed MSS approach is more suitable for complex systems such as CNCMT rather than the binary-state system approach.Originality/valueThis paper presents a generalized framework MSS's failure and repair data analysis has been developed and applied to a CNCMT.
期刊介绍:
In today''s competitive business and industrial environment, it is essential to have an academic journal offering the most current theoretical knowledge on quality and reliability to ensure that top management is fully conversant with new thinking, techniques and developments in the field. The International Journal of Quality & Reliability Management (IJQRM) deals with all aspects of business improvements and with all aspects of manufacturing and services, from the training of (senior) managers, to innovations in organising and processing to raise standards of product and service quality. It is this unique blend of theoretical knowledge and managerial relevance that makes IJQRM a valuable resource for managers striving for higher standards.Coverage includes: -Reliability, availability & maintenance -Gauging, calibration & measurement -Life cycle costing & sustainability -Reliability Management of Systems -Service Quality -Green Marketing -Product liability -Product testing techniques & systems -Quality function deployment -Reliability & quality education & training -Productivity improvement -Performance improvement -(Regulatory) standards for quality & Quality Awards -Statistical process control -System modelling -Teamwork -Quality data & datamining